{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quora: X label rotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.quora.com/I-am-drawing-the-boxplot-using-Python-but-I-want-the-labels-in-the-x-axis-to-be-displayed-vertically-rather-than-horizontally-How-do-I-do-this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "data = load_iris()\n",
    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "\n",
    "sepalValues = df['sepal length (cm)'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize=(10, 5))\n",
    "\n",
    "# rectangular box plot\n",
    "bplot = axes.boxplot(sepalValues, labels = ['My Long Label Name'])\n",
    "\n",
    "plt.xticks(rotation = 90);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Option 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize=(10, 5))\n",
    "\n",
    "bplot = axes.boxplot(sepalValues, labels = ['My Long Label Name'])\n",
    "\n",
    "axes.xaxis.set_tick_params(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Option 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize=(10, 5))\n",
    "\n",
    "bplot = axes.boxplot(sepalValues, labels = ['My Long Label Name'])\n",
    "\n",
    "for label in axes.get_xticklabels():\n",
    "    label.set_rotation(90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stack Overflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to change the length of the cap of a whisker in matplotlib boxplot: https://stackoverflow.com/questions/50996368/how-to-change-the-length-of-the-cap-of-a-whisker-in-matplotlib-boxplot?rq=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mu, sigma = 0, 1 # mean and standard deviation\n",
    "s = np.random.normal(mu, sigma, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize=(10, 5))\n",
    "\n",
    "normal_caps = axes[0].boxplot(s, labels = ['Normal Caps'],\n",
    "                    capprops = dict(linestyle='-', linewidth=2, color='Black'))\n",
    "\n",
    "big_caps = axes[1].boxplot(s, labels = ['Longer Caps'],\n",
    "                    capprops = dict(linestyle='-', linewidth=2, color='Black'))\n",
    "\n",
    "for cap in big_caps['caps']:\n",
    "    cap.set_xdata(cap.get_xdata() + np.array([-.15,.15]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to change the linestyle of whiskers in pandas boxplots: https://stackoverflow.com/questions/46226032/how-to-change-the-linestyle-of-whiskers-in-pandas-boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "mu, sigma = 0, 1 \n",
    "s = np.random.normal(mu, sigma, 1000)\n",
    "\n",
    "df = pd.DataFrame(s)\n",
    "\n",
    "bPlot = df.boxplot(whiskerprops = dict(linestyle='--'\n",
    "                               , linewidth=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py36]",
   "language": "python",
   "name": "conda-env-py36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
